class_docstr: This dataset contains the PG-19 language modeling benchmark.
docstr: Returns SplitGenerators.
docstr_tok: ["Returns","SplitGenerators","."]
code: def _split_generators(self, dl_manager):
      """Returns SplitGenerators."""
      del dl_manager  # Unused
  
      metadata_dict = dict()
      metadata_path = os.path.join(_DATA_DIR, "metadata.csv")
      metadata = tf.io.gfile.GFile(metadata_path).read().splitlines()
  
      for row in metadata:
          row_split = row.split(",")
          # book_id: [book_title, publication_date, book_link]
          metadata_dict[int(row_split[0])] = row_split[1:]
  
      return [
          tfds.core.SplitGenerator(
              name=tfds.Split.TRAIN,
              gen_kwargs={
                  "metadata": metadata_dict,
                  "filepath": os.path.join(_DATA_DIR, "train"),
              },
          ),
          tfds.core.SplitGenerator(
              name=tfds.Split.VALIDATION,
              gen_kwargs={
                  "metadata": metadata_dict,
                  "filepath": os.path.join(_DATA_DIR, "validation"),
              },
          ),
          tfds.core.SplitGenerator(
              name=tfds.Split.TEST,
              gen_kwargs={
                  "metadata": metadata_dict,
                  "filepath": os.path.join(_DATA_DIR, "test"),
              },
          ),
      ]
  
file_path: datasets/tensorflow_datasets/datasets/pg19/pg19_dataset_builder.py
imports: from __future__ import annotations
  import os
  import numpy
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
  import tensorflow_datasets.public_api
label_desc: Use image node_id 2 for calling the Builder obj's underlying member method code with example usage: obj._split_generators(dl_manager) and returns: list